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Determinants of operative time in thyroid surgery: A prospective multicenter study of 3454 thyroidectomies
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Edité par CCSD ; Public Library of Science -
on behalf of the CATHY Study Group. International audience. Objective: To identify the determinants of operative time for thyroidectomy and quantify the relative influence of preoperative and intra-operative factors.Background: Anticipation of operative time is key to avoid both waste of hospital resources and dissatisfaction of the surgical staff. Having an accurate and anticipated planning would allow a rationalized operating room use and may improve patient flow and staffing level.Methods: We conducted a prospective, cross-sectional study between April 2008 and December 2009. The operative time of 3454 patients who underwent thyroidectomy performed by 28 surgeons in five academic hospitals was monitored. We used multilevel linear regression to model determinants of operative time while accounting for the interplay of characteristics specific to surgeons, patients, and surgical procedures. The relative impact of each variable on operative time was estimated.Results: Overall, 86% (99% CI 83 to 89) of operative time variation was related to preoperative variables. Surgeon characteristics accounted for 32% (99% CI 29 to 35) of variation, center location for 29% (99% CI 25 to 33), and surgical procedure or patient variables for 24% (99% CI 20 to 27). Operative time was significantly lower among experienced surgeons having practiced from 5–19 years (-21.8 min, P<0.05), performing at least 300 thyroidectomies per year (-28.8 min, P<0.05), and with increasing number of thyroidectomies performed the same day (-11.7min, P<0.001). Conversely, operative time increased in cases of procedure supervision by a more experienced surgeon (+20.0 min, P<0.001). The remaining 13.0% of variability was attributable to unanticipated technical difficulties at the time of surgery.Conclusions: Variation in thyroidectomy duration is largely explained by preoperative factors, suggesting that it can be accurately anticipated. Prediction tools allowing better regulation of patient flow in operating rooms appears feasible for both working conditions and cost management.
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